code: def main():
      parser = argparse.ArgumentParser()
      add_service_args(parser)
      parser.add_argument(
          "-c",
          "--conf-paths",
          nargs="+",
          help="Where to read RunSpecs to run from",
          default=[],
      )
      parser.add_argument(
          "--models-to-run",
          nargs="+",
          help="Only RunSpecs with these models specified. If no model is specified, runs with all models.",
          default=None,
      )
      parser.add_argument(
          "--groups-to-run",
          nargs="+",
          help="Only RunSpecs with these (scenario) groups specified. "
          "If no group is specified, runs with all groups.",
          default=None,
      )
      parser.add_argument(
          "--exit-on-error",
          action="store_true",
          default=None,
          help="Fail and exit immediately if a particular RunSpec fails.",
      )
      parser.add_argument(
          "--skip-completed-runs",
          action="store_true",
          default=None,
          help="Skip RunSpecs that have completed i.e. output files exists.",
      )
      parser.add_argument(
          "--priority",
          type=int,
          default=None,
          help="Run RunSpecs with priority less than or equal to this number. "
          "If a value for --priority is not specified, run on everything",
      )
      parser.add_argument(
          "-r",
          "--run-specs",
          nargs="*",
          help="Specifies what to run",
          default=[],
      )
      parser.add_argument(
          "--enable-huggingface-models",
          nargs="+",
          default=[],
          help="Experimental: Enable using AutoModelForCausalLM models from Hugging Face Model Hub. "
          "Format: namespace/model_name[@revision]",
      )
      parser.add_argument(
          "--enable-remote-models",
          nargs="+",
          default=[],
          help="Experimental: Enable remote service models that are not available on the client. "
          "The client will use RemoteWindowService for windowing. "
          "Format: namespace/model_name[@revision]",
      )
      add_run_args(parser)
      args = parser.parse_args()
      validate_args(args)
  
      for huggingface_model_name in args.enable_huggingface_models:
          register_huggingface_model_config(huggingface_model_name)
  
      if not args.local:
          check_and_register_remote_model(
              args.server_url, args.enable_remote_models
          )
  
      run_entries: List[RunEntry] = []
      if args.conf_paths:
          run_entries.extend(read_run_entries(args.conf_paths).entries)
      if args.run_specs:
          run_entries.extend(
              [
                  RunEntry(
                      description=description, priority=1, groups=None
                  )
                  for description in args.run_specs
              ]
          )
  
      run_specs = run_entries_to_run_specs(
          run_entries=run_entries,
          max_eval_instances=args.max_eval_instances,
          num_train_trials=args.num_train_trials,
          models_to_run=args.models_to_run,
          groups_to_run=args.groups_to_run,
          priority=args.priority,
      )
      hlog(
          f"{len(run_entries)} entries produced {len(run_specs)} run specs"
      )
  
      if len(run_specs) == 0:
          hlog("There were no RunSpecs or they got filtered out.")
          return
  
      auth: Authentication = (
          Authentication("")
          if args.skip_instances or args.local
          else create_authentication(args)
      )
  
      run_benchmarking(
          run_specs=run_specs,
          auth=auth,
          url=args.server_url,
          local=args.local,
          local_path=args.local_path,
          num_threads=args.num_threads,
          output_path=args.output_path,
          suite=args.suite,
          dry_run=args.dry_run,
          skip_instances=args.skip_instances,
          cache_instances=args.cache_instances,
          cache_instances_only=args.cache_instances_only,
          skip_completed_runs=args.skip_completed_runs,
          exit_on_error=args.exit_on_error,
          mongo_uri=args.mongo_uri,
      )
  
      hlog("Done.")
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/run.py
imports: import argparse
  from dataclasses import replace
  from typing import List, Optional
  from helm.benchmark.presentation.run_entry import RunEntry, read_run_entries
  from helm.common.hierarchical_logger import hlog, htrack, htrack_block
  from helm.common.authentication import Authentication
  from helm.common.object_spec import parse_object_spec
  from helm.proxy.clients.huggingface_model_registry import register_huggingface_model_config
  from helm.proxy.clients.remote_model_registry import check_and_register_remote_model
  from helm.proxy.services.remote_service import create_authentication, add_service_args
  from helm.benchmark.adaptation.adapter_spec import AdapterSpec
  from .executor import ExecutionSpec
  from .runner import Runner, RunSpec, LATEST_SYMLINK
  from .run_specs import construct_run_specs
label_desc: Use image node_id 5 for calling a global function with example usage: main() without return types